Significance of molecular classification of ependymomas: C11orf95-RELA fusion-negative supratentorial ependymomas are a heterogeneous group of tumors
Bibliographic record
Abstract
Extensive molecular analyses of ependymal tumors have revealed that supratentorial and posterior fossa ependymomas have distinct molecular profiles and are likely to be different diseases. The presence of C11orf95-RELA fusion genes in a subset of supratentorial ependymomas (ST-EPN) indicated the existence of molecular subgroups. However, the pathogenesis of RELA fusion-negative ependymomas remains elusive. To investigate the molecular pathogenesis of these tumors and validate the molecular classification of ependymal tumors, we conducted thorough molecular analyses of 113 locally diagnosed ependymal tumors from 107 patients in the Japan Pediatric Molecular Neuro-Oncology Group. All tumors were histopathologically reviewed and 12 tumors were re-classified as non-ependymomas. A combination of RT-PCR, FISH, and RNA sequencing identified RELA fusion in 19 of 29 histologically verified ST-EPN cases, whereas another case was diagnosed as ependymoma RELA fusion-positive via the methylation classifier (68.9%). Among the 9 RELA fusion-negative ST-EPN cases, either the YAP1 fusion, BCOR tandem duplication, EP300-BCORL1 fusion, or FOXO1-STK24 fusion was detected in single cases. Methylation classification did not identify a consistent molecular class within this group. Genome-wide methylation profiling successfully sub-classified posterior fossa ependymoma (PF-EPN) into PF-EPN-A (PFA) and PF-EPN-B (PFB). A multivariate analysis using Cox regression confirmed that PFA was the sole molecular marker which was independently associated with patient survival. A clinically applicable pyrosequencing assay was developed to determine the PFB subgroup with 100% specificity using the methylation status of 3 genes, CRIP1, DRD4 and LBX2. Our results emphasized the significance of molecular classification in the diagnosis of ependymomas. RELA fusion-negative ST-EPN appear to be a heterogeneous group of tumors that do not fall into any of the existing molecular subgroups and are unlikely to form a single category.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".